diff --git a/examples/regular/models/creditcard_cgan.py b/examples/regular/models/creditcard_cgan.py
index 9522b3b4..052163ca 100644
--- a/examples/regular/models/creditcard_cgan.py
+++ b/examples/regular/models/creditcard_cgan.py
@@ -21,7 +21,6 @@
                 'V3', 'V22', 'V6', 'V20', 'V27', 'V16', 'V13', 'V25', 'V24', 'V18', 'V2', 'V1', 'V5', 'V15',
                 'V9', 'V23', 'Class']
 processed_data = data[ sorted_cols ].copy()
-processed_data['Class'] = processed_data['Class'].apply(lambda x: 1 if x == "'1'" else 0)
 
 #For the purpose of this example we will only synthesize the minority class
 train_data = processed_data.loc[processed_data['Class'] == 1].copy()
@@ -47,7 +46,7 @@
 beta_2 = 0.9
 
 log_step = 100
-epochs = 500 + 1
+epochs = 2 + 1
 learning_rate = 5e-4
 models_dir = '../cache'
 
diff --git a/examples/regular/models/creditcard_cramergan.py b/examples/regular/models/creditcard_cramergan.py
index 0eef4acd..a05782d8 100644
--- a/examples/regular/models/creditcard_cramergan.py
+++ b/examples/regular/models/creditcard_cramergan.py
@@ -22,7 +22,6 @@
 print('Dataset columns: {}'.format(num_cols))
 sorted_cols = ['V14', 'V4', 'V10', 'V17', 'V12', 'V26', 'Amount', 'V21', 'V8', 'V11', 'V7', 'V28', 'V19', 'V3', 'V22', 'V6', 'V20', 'V27', 'V16', 'V13', 'V25', 'V24', 'V18', 'V2', 'V1', 'V5', 'V15', 'V9', 'V23', 'Class']
 processed_data = data[ sorted_cols ].copy()
-processed_data['Class'] = processed_data['Class'].apply(lambda x: 1 if x == "'1'" else 0)
 
 #For the purpose of this example we will only synthesize the minority class
 train_data = processed_data.loc[processed_data['Class'] == 1].copy()
@@ -45,7 +44,7 @@
 batch_size = 128
 
 log_step = 100
-epochs = 2+1
+epochs = 500+1
 learning_rate = 5e-4
 beta_1 = 0.5
 beta_2 = 0.9
diff --git a/examples/regular/models/creditcard_ctgan.py b/examples/regular/models/creditcard_ctgan.py
index a8a51fe0..e79f7a36 100644
--- a/examples/regular/models/creditcard_ctgan.py
+++ b/examples/regular/models/creditcard_ctgan.py
@@ -21,7 +21,6 @@
                 'V3', 'V22', 'V6', 'V20', 'V27', 'V16', 'V13', 'V25', 'V24', 'V18', 'V2', 'V1', 'V5', 'V15',
                 'V9', 'V23', 'Class']
 processed_data = data[ sorted_cols ].copy()
-processed_data['Class'] = processed_data['Class'].apply(lambda x: 1 if x == "'1'" else 0)
 
 # For the purpose of this example we will only synthesize the minority class
 train_data = processed_data.loc[processed_data['Class'] == 1].copy()
diff --git a/examples/regular/models/creditcard_cwgangp.py b/examples/regular/models/creditcard_cwgangp.py
index 5f9871f6..911f390e 100644
--- a/examples/regular/models/creditcard_cwgangp.py
+++ b/examples/regular/models/creditcard_cwgangp.py
@@ -17,7 +17,6 @@
 print('Dataset columns: {}'.format(num_cols))
 sorted_cols = ['V14', 'V4', 'V10', 'V17', 'V12', 'V26', 'Amount', 'V21', 'V8', 'V11', 'V7', 'V28', 'V19', 'V3', 'V22', 'V6', 'V20', 'V27', 'V16', 'V13', 'V25', 'V24', 'V18', 'V2', 'V1', 'V5', 'V15', 'V9', 'V23', 'Class']
 processed_data = data[ sorted_cols ].copy()
-processed_data['Class'] = processed_data['Class'].apply(lambda x: 1 if x == "'1'" else 0)
 
 #For the purpose of this example we will only synthesize the minority class
 train_data = processed_data.loc[processed_data['Class'] == 1].copy()
diff --git a/examples/regular/models/creditcard_wgan.py b/examples/regular/models/creditcard_wgan.py
index 74536cb4..073ba138 100644
--- a/examples/regular/models/creditcard_wgan.py
+++ b/examples/regular/models/creditcard_wgan.py
@@ -19,7 +19,6 @@
 print('Dataset columns: {}'.format(num_cols))
 sorted_cols = ['V14', 'V4', 'V10', 'V17', 'V12', 'V26', 'Amount', 'V21', 'V8', 'V11', 'V7', 'V28', 'V19', 'V3', 'V22', 'V6', 'V20', 'V27', 'V16', 'V13', 'V25', 'V24', 'V18', 'V2', 'V1', 'V5', 'V15', 'V9', 'V23', 'Class']
 processed_data = data[ sorted_cols ].copy()
-processed_data['Class'] = processed_data['Class'].apply(lambda x: 1 if x == "'1'" else 0)
 
 #For the purpose of this example we will only synthesize the minority class
 train_data = processed_data.loc[processed_data['Class'] == 1].copy()
@@ -41,7 +40,7 @@
 batch_size = 128
 
 log_step = 100
-epochs = 300+1
+epochs = 500+1
 learning_rate = 5e-4
 beta_1 = 0.5
 beta_2 = 0.9